app.py

import spaces
import gradio as gr
import torch
from PIL import Image
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration, pipeline
from diffusers import DiffusionPipeline
import random
import numpy as np
import os
import subprocess
from qwen_vl_utils import process_vision_info
from threading import Thread
import uuid
import io

# Initialize models
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16

huggingface_token = os.getenv("HUGGINGFACE_TOKEN")

# FLUX.1-dev model
pipe = DiffusionPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-dev", torch_dtype=dtype, token=huggingface_token
).to(device)

# Initialize Qwen2VL model
qwen_model = Qwen2VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/JSONify-Flux", trust_remote_code=True, torch_dtype=torch.float16
).to(device).eval()
qwen_processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux", trust_remote_code=True)

# Prompt Enhancer
enhancer_long = pipeline("summarization", model="gokaygokay/Lamini-Prompt-Enchance-Long", device=device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024  # Reduced to prevent memory issues

# Qwen2VL caption function
@spaces.GPU
def qwen_caption(image):
    # Convert image to PIL if it's not already
    if not isinstance(image, Image.Image):
        image = Image.fromarray(image)
    
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": image},
                {"type": "text", "text": "Caption the image"},
            ],
        }
    ]

    text = qwen_processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)
    inputs = qwen_processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    ).to(device)

    generated_ids = qwen_model.generate(**inputs, max_new_tokens=1024)
    generated_ids_trimmed = [
        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
    ]
    output_text = qwen_processor.batch_decode(
        generated_ids_trimmed,
        skip_special_tokens=True,
        clean_up_tokenization_spaces=False,
    )[0]
    
    return output_text

# Prompt Enhancer function
def enhance_prompt(input_prompt):
    result = enhancer_long("Enhance the description: " + input_prompt)
    enhanced_text = result[0]['summary_text']
    return enhanced_text

@spaces.GPU(duration=190)
def process_workflow(image, text_prompt, use_enhancer, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
    if image is not None:
        # Convert image to PIL if it's not already
        if not isinstance(image, Image.Image):
            image = Image.fromarray(image)
        
        prompt = qwen_caption(image)
        print(prompt)
    else:
        prompt = text_prompt
    
    if use_enhancer:
        prompt = enhance_prompt(prompt)
    
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)
    
    generator = torch.Generator(device=device).manual_seed(seed)
    
    # Reduce memory usage by clearing GPU cache
    torch.cuda.empty_cache()
    
    # Generate image with FLUX.1-dev
    try:
        image = pipe(
            prompt=prompt,
            generator=generator,
            num_inference_steps=num_inference_steps,
            width=width,
            height=height,
            guidance_scale=guidance_scale
        ).images[0]
    except RuntimeError as e:
        if "CUDA out of memory" in str(e):
            raise RuntimeError("CUDA out of memory. Try reducing image size or inference steps.")
        else:
            raise e
    
    return image, prompt, seed

custom_css = """
.input-group, .output-group {
    border: 1px solid #e0e0e0;
    border-radius: 10px;
    padding: 20px;
    margin-bottom: 20px;
    background-color: #f9f9f9;
}
.submit-btn {
    background-color: #2980b9 !important;
    color: white !important;
}
.submit-btn:hover {
    background-color: #3498db !important;
}
"""

title = """<h1 align="center">FLUX.1-dev with Qwen2VL Captioner and Prompt Enhancer</h1>
<p><center>
<a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" target="_blank">[FLUX.1-dev Model]</a>
<a href="https://huggingface.co/prithivMLmods/JSONify-Flux" target="_blank">[Qwen2VL Model]</a>
<a href="https://huggingface.co/gokaygokay/Lamini-Prompt-Enchance-Long" target="_blank">[Prompt Enhancer Long]</a>
<p align="center">Create long prompts from images or enhance your short prompts with prompt enhancer</p>
</center></p>
"""

with gr.Blocks(css=custom_css, theme=gr.themes.Soft(primary_hue="blue", secondary_hue="gray")) as demo:
    gr.HTML(title)
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="input-group"):
                input_image = gr.Image(label="Input Image (Qwen2VL Captioner)")
            
            with gr.Accordion("Advanced Settings", open=False):
                text_prompt = gr.Textbox(label="Text Prompt (optional, used if no image is uploaded)")
                use_enhancer = gr.Checkbox(label="Use Prompt Enhancer", value=False)
                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)  # Reduced default width
                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512)  # Reduced default height
                guidance_scale = gr.Slider(label="Guidance Scale", minimum=1, maximum=15, step=0.1, value=3.5)
                num_inference_steps = gr.Slider(label="Inference Steps", minimum=1, maximum=50, step=1, value=20)  # Reduced default steps
            
            generate_btn = gr.Button("Generate Image", elem_classes="submit-btn")
        
        with gr.Column(scale=1):
            with gr.Group(elem_classes="output-group"):
                output_image = gr.Image(label="Result", elem_id="gallery", show_label=False)
                final_prompt = gr.Textbox(label="Final Prompt Used")
                used_seed = gr.Number(label="Seed Used")
    
    generate_btn.click(
        fn=process_workflow,
        inputs=[
            input_image, text_prompt, use_enhancer, seed, randomize_seed,
            width, height, guidance_scale, num_inference_steps
        ],
        outputs=[output_image, final_prompt, used_seed]
    )

demo.launch(debug=True)